approved the final draft. Data Availability The following information was supplied regarding data availability: The code is available at Zenodo: lizh0019. (2025). lizh0019/cassava: Cassava leaf disease recognition (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.14739855 . The cassava leaf disease dataset is available at Kaggle: https://www.kaggle.com/competitions/cassava-leaf-disease-classification/data . References Ahmed, Jones & Marks (2015) Ahmed E Jones M Marks TK An improved deep learning architecture for person re-identification 2015 Computer Vision and Pattern Recognition Piscataway IEEE 3908 3916 Amid et al. (2019) Amid E Warmuth MK Anil R Koren T Robust bi-tempered logistic loss bas
Open resource ↗Kaggle · cassava-leaf-disease-classification · lines:1680-1767Unverified paper record
Automatic cassava disease recognition using object segmentation and progressive learning.
PeerJ. Computer science · 18 Mar 2025 · 10.7717/peerj-cs.2721
Abstract
Cassava is a vital crop for millions of farmers worldwide, but its cultivation is threatened by various destructive diseases. Current detection methods for cassava diseases are costly, time-consuming, and often limited to controlled environments, making them unsuitable for large-scale agricultural use. This study aims to develop a deep learning framework that enables early, accurate, and efficient detection of cassava diseases in real-world conditions. We propose a self-supervised object segmentation technique, combined with a progressive learning algorithm (PLA) that incorporates both triplet loss and classification loss to learn robust feature embeddings. Our approach achieves superior performance on the Cassava Leaf Disease Classification (CLDC) dataset from the Kaggle competition, with an accuracy of 91.43%, outperforming all other participants. The proposed method offers a practical and efficient solution for cassava disease detection, demonstrating the potential for large-scale, real-world application in agriculture.
Plant phenotyping relevance
カシャバ葉の病害状態を画像から推定するセグメンテーションと深層学習手法の開発が研究の中心であり、植物病害フェノタイピングに該当する。
abstractWe propose a self-supervised object segmentation technique, combined with a progressive learning algorithm (PLA) that incorporates both triplet loss and classification loss to learn robust feature embeddings.
abstractThe proposed method offers a practical and efficient solution for cassava disease detection
Code and data availability
The paper uses the public Kaggle Cassava Leaf Disease Classification dataset (21,367 labeled cassava leaf images) and releases the authors' analysis code on Zenodo; both are paper-specific, public, and actionable.
ang conceived and designed the experiments, analyzed the data, prepared figures and/or tables, and approved the final draft. Data Availability The following information was supplied regarding data availability: The code is available at Zenodo: lizh0019. (2025). lizh0019/cassava: Cassava leaf disease recognition (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.14739855 . The cassava leaf disease dataset is available at Kaggle: https://www.kaggle.com/competitions/cassava-leaf-disease-classification/data . References Ahmed, Jones & Marks (2015) Ahmed E Jones M Marks TK An improved deep learning architecture for person re-identification 2015 Computer Vision and Pattern Recognition Piscataway IEE
Open resource ↗Zenodo · 10.5281/zenodo.14739855 · lines:1680-1767This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.